{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u29IpvpwJlmh"
      },
      "source": [
        "**Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MYTXoiSGJUWr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import cv2\n",
        "import math\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import shutil\n",
        "from sklearn.preprocessing import QuantileTransformer\n",
        "from PIL import Image\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQULAOHTJshq"
      },
      "source": [
        "**Load Dataset from Google Drive**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "aPhAcRw4JvCD",
        "outputId": "d3aef362-e34e-4f7c-ca48-7e761573b756"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>arp.opcode</th>\n",
              "      <th>arp.hw.size</th>\n",
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              "      <th>icmp.seq_le</th>\n",
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              "      <th>http.content_length</th>\n",
              "      <th>http.response</th>\n",
              "      <th>http.tls_port</th>\n",
              "      <th>tcp.ack</th>\n",
              "      <th>tcp.ack_raw</th>\n",
              "      <th>...</th>\n",
              "      <th>mqtt.conack.flags-1471199</th>\n",
              "      <th>mqtt.conack.flags-1574358</th>\n",
              "      <th>mqtt.conack.flags-1574359</th>\n",
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              "      <th>mqtt.protoname-0.0</th>\n",
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              "      <th>mqtt.topic-0</th>\n",
              "      <th>mqtt.topic-0.0</th>\n",
              "      <th>mqtt.topic-Temperature_and_Humidity</th>\n",
              "      <th>Attack_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>4.189800e+09</td>\n",
              "      <td>...</td>\n",
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              "      <td>Normal</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.069571e+09</td>\n",
              "      <td>1.069571e+09</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>DDoS_TCP</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.900000e+01</td>\n",
              "      <td>1.200409e+09</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 96 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53fc54a5-d529-4393-9a8c-b73e7e55a566')\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-53fc54a5-d529-4393-9a8c-b73e7e55a566 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-53fc54a5-d529-4393-9a8c-b73e7e55a566');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   arp.opcode  arp.hw.size  icmp.checksum  icmp.seq_le  icmp.unused  \\\n",
              "0         0.0          0.0            0.0          0.0          0.0   \n",
              "1         0.0          0.0            0.0          0.0          0.0   \n",
              "2         0.0          0.0            0.0          0.0          0.0   \n",
              "3         0.0          0.0            0.0          0.0          0.0   \n",
              "4         0.0          0.0            0.0          0.0          0.0   \n",
              "\n",
              "   http.content_length  http.response  http.tls_port       tcp.ack  \\\n",
              "0                  0.0            0.0            0.0  8.700000e+01   \n",
              "1                  0.0            0.0            0.0  1.000000e+00   \n",
              "2                  0.0            0.0            0.0  1.069571e+09   \n",
              "3                  0.0            0.0            0.0  0.000000e+00   \n",
              "4                  0.0            0.0            0.0  5.900000e+01   \n",
              "\n",
              "    tcp.ack_raw  ...  mqtt.conack.flags-1471199  mqtt.conack.flags-1574358  \\\n",
              "0  1.093946e+09  ...                          0                          0   \n",
              "1  4.189800e+09  ...                          0                          0   \n",
              "2  1.069571e+09  ...                          0                          0   \n",
              "3  0.000000e+00  ...                          0                          0   \n",
              "4  1.200409e+09  ...                          0                          0   \n",
              "\n",
              "   mqtt.conack.flags-1574359  mqtt.protoname-0  mqtt.protoname-0.0  \\\n",
              "0                          0                 0                   1   \n",
              "1                          0                 1                   0   \n",
              "2                          0                 0                   1   \n",
              "3                          0                 1                   0   \n",
              "4                          0                 1                   0   \n",
              "\n",
              "   mqtt.protoname-MQTT  mqtt.topic-0  mqtt.topic-0.0  \\\n",
              "0                    0             0               1   \n",
              "1                    0             1               0   \n",
              "2                    0             0               1   \n",
              "3                    0             1               0   \n",
              "4                    0             1               0   \n",
              "\n",
              "   mqtt.topic-Temperature_and_Humidity  Attack_type  \n",
              "0                                    0     Password  \n",
              "1                                    0       Normal  \n",
              "2                                    0     DDoS_TCP  \n",
              "3                                    0       Normal  \n",
              "4                                    0       Normal  \n",
              "\n",
              "[5 rows x 96 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df =pd.read_csv(\"/content/drive/MyDrive/processed_IIoT.csv\")\n",
        "#del df[df.columns[0]]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ka3D0oL7aisD",
        "outputId": "f7074532-8134-4287-ed9c-71c7a3fad730"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1921663, 96)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CpqVjAbqJ-u4"
      },
      "outputs": [],
      "source": [
        "# Transform all features into the scale of [0,1]\n",
        "numeric_features = df.dtypes[df.dtypes != 'object'].index\n",
        "scaler = QuantileTransformer() \n",
        "df[numeric_features] = scaler.fit_transform(df[numeric_features])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "rQodKd-CJ_do",
        "outputId": "e6a479c6-ad5a-4737-83b5-1112c3907a1a"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>arp.opcode</th>\n",
              "      <th>arp.hw.size</th>\n",
              "      <th>icmp.checksum</th>\n",
              "      <th>icmp.seq_le</th>\n",
              "      <th>icmp.unused</th>\n",
              "      <th>http.content_length</th>\n",
              "      <th>http.response</th>\n",
              "      <th>http.tls_port</th>\n",
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              "      <th>tcp.ack_raw</th>\n",
              "      <th>...</th>\n",
              "      <th>mqtt.conack.flags-1471198</th>\n",
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              "      <th>mqtt.protoname-0</th>\n",
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              "      <th>mqtt.protoname-MQTT</th>\n",
              "      <th>mqtt.topic-0</th>\n",
              "      <th>mqtt.topic-0.0</th>\n",
              "      <th>mqtt.topic-Temperature_and_Humidity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1921663.0</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1921663.0</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>...</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "      <td>1.921663e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.355961e-03</td>\n",
              "      <td>8.359039e-03</td>\n",
              "      <td>9.597278e+00</td>\n",
              "      <td>1.063151e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.814512e+00</td>\n",
              "      <td>4.326339e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.237652e+02</td>\n",
              "      <td>1.242883e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>2.653951e-04</td>\n",
              "      <td>2.653951e-04</td>\n",
              "      <td>1.194278e-03</td>\n",
              "      <td>1.194278e-03</td>\n",
              "      <td>1.699829e+02</td>\n",
              "      <td>7.400079e+01</td>\n",
              "      <td>1.101629e+01</td>\n",
              "      <td>1.699833e+02</td>\n",
              "      <td>7.400079e+01</td>\n",
              "      <td>1.101589e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.459344e+00</td>\n",
              "      <td>1.459882e+00</td>\n",
              "      <td>4.803800e+01</td>\n",
              "      <td>5.042413e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.783635e+01</td>\n",
              "      <td>3.293174e+01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.890822e+01</td>\n",
              "      <td>7.911675e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>2.601456e-01</td>\n",
              "      <td>2.601456e-01</td>\n",
              "      <td>5.518511e-01</td>\n",
              "      <td>5.518511e-01</td>\n",
              "      <td>1.202142e+02</td>\n",
              "      <td>1.157328e+02</td>\n",
              "      <td>5.184396e+01</td>\n",
              "      <td>1.202141e+02</td>\n",
              "      <td>1.157328e+02</td>\n",
              "      <td>5.184306e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.636637e+01</td>\n",
              "      <td>6.471557e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.231607e+02</td>\n",
              "      <td>1.285834e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.916967e+02</td>\n",
              "      <td>1.918092e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "      <td>2.550000e+02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 95 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-290efe96-5511-463c-af6c-f0e7ab5f470c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-290efe96-5511-463c-af6c-f0e7ab5f470c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-290efe96-5511-463c-af6c-f0e7ab5f470c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         arp.opcode   arp.hw.size  icmp.checksum   icmp.seq_le  icmp.unused  \\\n",
              "count  1.921663e+06  1.921663e+06   1.921663e+06  1.921663e+06    1921663.0   \n",
              "mean   8.355961e-03  8.359039e-03   9.597278e+00  1.063151e+01          0.0   \n",
              "std    1.459344e+00  1.459882e+00   4.803800e+01  5.042413e+01          0.0   \n",
              "min    0.000000e+00  0.000000e+00   0.000000e+00  0.000000e+00          0.0   \n",
              "25%    0.000000e+00  0.000000e+00   0.000000e+00  0.000000e+00          0.0   \n",
              "50%    0.000000e+00  0.000000e+00   0.000000e+00  0.000000e+00          0.0   \n",
              "75%    0.000000e+00  0.000000e+00   0.000000e+00  0.000000e+00          0.0   \n",
              "max    2.550000e+02  2.550000e+02   2.550000e+02  2.550000e+02          0.0   \n",
              "\n",
              "       http.content_length  http.response  http.tls_port       tcp.ack  \\\n",
              "count         1.921663e+06   1.921663e+06      1921663.0  1.921663e+06   \n",
              "mean          5.814512e+00   4.326339e+00            0.0  1.237652e+02   \n",
              "std           3.783635e+01   3.293174e+01            0.0  7.890822e+01   \n",
              "min           0.000000e+00   0.000000e+00            0.0  0.000000e+00   \n",
              "25%           0.000000e+00   0.000000e+00            0.0  6.636637e+01   \n",
              "50%           0.000000e+00   0.000000e+00            0.0  1.231607e+02   \n",
              "75%           0.000000e+00   0.000000e+00            0.0  1.916967e+02   \n",
              "max           2.550000e+02   2.550000e+02            0.0  2.550000e+02   \n",
              "\n",
              "        tcp.ack_raw  ...  mqtt.conack.flags-1471198  \\\n",
              "count  1.921663e+06  ...               1.921663e+06   \n",
              "mean   1.242883e+02  ...               2.653951e-04   \n",
              "std    7.911675e+01  ...               2.601456e-01   \n",
              "min    0.000000e+00  ...               0.000000e+00   \n",
              "25%    6.471557e+01  ...               0.000000e+00   \n",
              "50%    1.285834e+02  ...               0.000000e+00   \n",
              "75%    1.918092e+02  ...               0.000000e+00   \n",
              "max    2.550000e+02  ...               2.550000e+02   \n",
              "\n",
              "       mqtt.conack.flags-1471199  mqtt.conack.flags-1574358  \\\n",
              "count               1.921663e+06               1.921663e+06   \n",
              "mean                2.653951e-04               1.194278e-03   \n",
              "std                 2.601456e-01               5.518511e-01   \n",
              "min                 0.000000e+00               0.000000e+00   \n",
              "25%                 0.000000e+00               0.000000e+00   \n",
              "50%                 0.000000e+00               0.000000e+00   \n",
              "75%                 0.000000e+00               0.000000e+00   \n",
              "max                 2.550000e+02               2.550000e+02   \n",
              "\n",
              "       mqtt.conack.flags-1574359  mqtt.protoname-0  mqtt.protoname-0.0  \\\n",
              "count               1.921663e+06      1.921663e+06        1.921663e+06   \n",
              "mean                1.194278e-03      1.699829e+02        7.400079e+01   \n",
              "std                 5.518511e-01      1.202142e+02        1.157328e+02   \n",
              "min                 0.000000e+00      0.000000e+00        0.000000e+00   \n",
              "25%                 0.000000e+00      0.000000e+00        0.000000e+00   \n",
              "50%                 0.000000e+00      2.550000e+02        0.000000e+00   \n",
              "75%                 0.000000e+00      2.550000e+02        2.550000e+02   \n",
              "max                 2.550000e+02      2.550000e+02        2.550000e+02   \n",
              "\n",
              "       mqtt.protoname-MQTT  mqtt.topic-0  mqtt.topic-0.0  \\\n",
              "count         1.921663e+06  1.921663e+06    1.921663e+06   \n",
              "mean          1.101629e+01  1.699833e+02    7.400079e+01   \n",
              "std           5.184396e+01  1.202141e+02    1.157328e+02   \n",
              "min           0.000000e+00  0.000000e+00    0.000000e+00   \n",
              "25%           0.000000e+00  0.000000e+00    0.000000e+00   \n",
              "50%           0.000000e+00  2.550000e+02    0.000000e+00   \n",
              "75%           0.000000e+00  2.550000e+02    2.550000e+02   \n",
              "max           2.550000e+02  2.550000e+02    2.550000e+02   \n",
              "\n",
              "       mqtt.topic-Temperature_and_Humidity  \n",
              "count                         1.921663e+06  \n",
              "mean                          1.101589e+01  \n",
              "std                           5.184306e+01  \n",
              "min                           0.000000e+00  \n",
              "25%                           0.000000e+00  \n",
              "50%                           0.000000e+00  \n",
              "75%                           0.000000e+00  \n",
              "max                           2.550000e+02  \n",
              "\n",
              "[8 rows x 95 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Multiply the feature values by 255 to transform them into the scale of [0,255]\n",
        "df[numeric_features] = df[numeric_features].apply(\n",
        "    lambda x: (x*255))\n",
        "\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xBWR9hY8KC1B"
      },
      "source": [
        "**Generate images corresponding to each class**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uAZTY2zEKFYA"
      },
      "outputs": [],
      "source": [
        "#Generate images corresponding to each class\n",
        "df0=df[df['Attack_type']=='Normal'].drop(['Attack_type'],axis=1)\n",
        "df1=df[df['Attack_type']=='DDoS_UDP'].drop(['Attack_type'],axis=1)\n",
        "df2=df[df['Attack_type']=='DDoS_ICMP'].drop(['Attack_type'],axis=1)\n",
        "df3=df[df['Attack_type']=='SQL_injection'].drop(['Attack_type'],axis=1)\n",
        "df4=df[df['Attack_type']=='DDoS_TCP'].drop(['Attack_type'],axis=1)\n",
        "df5=df[df['Attack_type']=='Vulnerability_scanner'].drop(['Attack_type'],axis=1)\n",
        "df6=df[df['Attack_type']=='Password'].drop(['Attack_type'],axis=1)\n",
        "df7=df[df['Attack_type']=='DDoS_HTTP'].drop(['Attack_type'],axis=1)\n",
        "df8=df[df['Attack_type']=='Uploading'].drop(['Attack_type'],axis=1)\n",
        "df9=df[df['Attack_type']=='Backdoor'].drop(['Attack_type'],axis=1)\n",
        "df10=df[df['Attack_type']=='Port_Scanning'].drop(['Attack_type'],axis=1)\n",
        "df11=df[df['Attack_type']=='XSS'].drop(['Attack_type'],axis=1)\n",
        "df12=df[df['Attack_type']=='Ransomware'].drop(['Attack_type'],axis=1)\n",
        "df13=df[df['Attack_type']=='Fingerprinting'].drop(['Attack_type'],axis=1)\n",
        "df14=df[df['Attack_type']=='MITM'].drop(['Attack_type'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oNQYRcT5KMbP"
      },
      "outputs": [],
      "source": [
        "# ***********Generate 95*95 color images for class 0 (Normal)************\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Normal/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df0)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df0.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uXkIjO4AK-mH"
      },
      "outputs": [],
      "source": [
        "# ***********Generate 95*95 color images for class 1 (DDoS_UDP)************\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_UDP/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df1)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df1.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-sV0dT0DLJ4x"
      },
      "outputs": [],
      "source": [
        "# ***********Generate 95*95 color images for class 2 (DDoS_ICMP)************\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_ICMP/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df2)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df2.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ds-8Q6NqLX3P"
      },
      "outputs": [],
      "source": [
        "# ***********Generate 95*95 color images for class 3 (SQL_injection )************\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/SQL_injection/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df3)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df3.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "niBowkksLgzn"
      },
      "outputs": [],
      "source": [
        "# ***********Generate 95*95 color images for class 4 (DDoS_TCP)************\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_TCP/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df4)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df4.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "039X-VJpLrbd"
      },
      "outputs": [],
      "source": [
        "# ***********Generate 95*95 color images for class 5 (Vulnerability_scanner)************\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Vulnerability_scanner/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df5)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df5.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pD9SnJFwL0sA"
      },
      "outputs": [],
      "source": [
        "# ***********Generate 95*95 color images for class 6 (Password)************\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Password/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df6)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df6.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bFw3iU9RMIGo"
      },
      "outputs": [],
      "source": [
        "# *Generate 95*95 color images for class 7 (DDoS_HTTP)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_HTTP/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df7)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df7.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iRKAL7txMQbo"
      },
      "outputs": [],
      "source": [
        "# *Generate 95*95 color images for class 8 (Uploading)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Uploading/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df8)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df8.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8eKHmtlyMacO"
      },
      "outputs": [],
      "source": [
        "# ********Generate 95*95 color images for class 9 (Backdoor)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Backdoor/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df9)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df9.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MabMuS9-Mmuf"
      },
      "outputs": [],
      "source": [
        "# ********Generate 95*95 color images for class 10 (Port_Scanning)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Port_Scanning/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df10)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df10.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []        \n",
        "       "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T42wZQzNMxip"
      },
      "outputs": [],
      "source": [
        "# ********Generate 95*95 color images for class 11 (XSS)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/XSS/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df11)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df11.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-hyxW0CRM7mZ"
      },
      "outputs": [],
      "source": [
        "# ********Generate 95*95 color images for class 12 (Ransomware)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Ransomware/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df12)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df12.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = [] "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VteSfKr-NGrv"
      },
      "outputs": [],
      "source": [
        "# ********Generate 95*95 color images for class 13 (Fingerprinting)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Fingerprinting/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df13)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df13.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LefMfUIzNPw6"
      },
      "outputs": [],
      "source": [
        "# ********Generate 95*95 color images for class 14 (MITM)***\n",
        "count=0\n",
        "ims = []\n",
        "\n",
        "image_path = \"/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/MITM/\"\n",
        "os.makedirs(image_path)\n",
        "\n",
        "for i in range(0, len(df14)):  \n",
        "    count=count+1\n",
        "    if count<=285: \n",
        "        im=df14.iloc[i].values\n",
        "        ims=np.append(ims,im)\n",
        "    else:\n",
        "        ims=np.array(ims).reshape(95,95,3)\n",
        "        array = np.array(ims, dtype=np.uint8)\n",
        "        new_image = Image.fromarray(array)\n",
        "        new_image.save(image_path+str(i)+'.png')\n",
        "        count=0\n",
        "        ims = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NKIRaWRcNZus"
      },
      "source": [
        "**Display samples for each category**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CTpPIcMvNaxn"
      },
      "outputs": [],
      "source": [
        "img1 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Normal/1002143.png')\n",
        "img2 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_UDP/108965.png')\n",
        "img3 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_ICMP/15157.png')\n",
        "img4 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/SQL_injection/14299.png')\n",
        "img5 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_TCP/13441.png')\n",
        "img6 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Vulnerability_scanner/13727.png')\n",
        "img7 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Password/12011.png')\n",
        "img8 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/DDoS_HTTP/16015.png')\n",
        "img9 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Uploading/15157.png')\n",
        "img10 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Backdoor/17731.png')\n",
        "img11 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Port_Scanning/12869.png')\n",
        "img12 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/XSS/4003.png')\n",
        "img13 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Ransomware/6863.png')\n",
        "img14 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/Fingerprinting/8007.png')\n",
        "img15 = Image.open('/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/MITM/2859.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "id": "_FqszlexO_B3",
        "outputId": "b8660b5f-1170-4fea-a194-8788f3669cb7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 15 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 15)) \n",
        "plt.subplot(3,5,1)\n",
        "plt.imshow(img1)\n",
        "plt.title(\"Normal\")\n",
        "plt.subplot(3,5,2)\n",
        "plt.imshow(img2)\n",
        "plt.title(\"DDoS_UDP\")\n",
        "plt.subplot(3,5,3)\n",
        "plt.imshow(img3)\n",
        "plt.title(\"DDoS_ICMP\")\n",
        "plt.subplot(3,5,4)\n",
        "plt.imshow(img4)\n",
        "plt.title(\"SQL_injection\")\n",
        "plt.subplot(3,5,5)\n",
        "plt.imshow(img5)\n",
        "plt.title(\"DDoS_TCP\")\n",
        "plt.subplot(3,5,6)\n",
        "plt.imshow(img6)\n",
        "plt.title(\"Vulnerability_scanner\")\n",
        "plt.subplot(3,5,7)\n",
        "plt.imshow(img7)\n",
        "plt.title(\"Password\")\n",
        "plt.subplot(3,5,8)\n",
        "plt.imshow(img8)\n",
        "plt.title(\"DDoS_HTTP\")\n",
        "plt.subplot(3,5,9)\n",
        "plt.imshow(img9)\n",
        "plt.title(\"Uploading\")\n",
        "plt.subplot(3,5,10)\n",
        "plt.imshow(img10)\n",
        "plt.title(\"Backdoor\")\n",
        "plt.subplot(3,5,11)\n",
        "plt.imshow(img11)\n",
        "plt.title(\"Port_Scanning\")\n",
        "plt.subplot(3,5,12)\n",
        "plt.imshow(img12)\n",
        "plt.title(\"XSS\")\n",
        "plt.subplot(3,5,13)\n",
        "plt.imshow(img13)\n",
        "plt.title(\"Ransomware\")\n",
        "plt.subplot(3,5,14)\n",
        "plt.imshow(img14)\n",
        "plt.title(\"Fingerprinting\")\n",
        "plt.subplot(3,5,15)\n",
        "plt.imshow(img15)\n",
        "plt.title(\"MITM\")\n",
        "\n",
        "plt.show()  # display it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGjSdd4oPLpA"
      },
      "source": [
        "**Split the training and test set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IY2Rv5unPO6u",
        "outputId": "cb93bb40-1229-48ef-a231-f8f1134b5ed6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6711\n",
            "Finish creating test set\n"
          ]
        }
      ],
      "source": [
        "# Create folders to store images\n",
        "Train_Dir='/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/'\n",
        "Val_Dir='/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/test/'\n",
        "allimgs=[]\n",
        "for subdir in os.listdir(Train_Dir):\n",
        "    for filename in os.listdir(os.path.join(Train_Dir,subdir)):\n",
        "        filepath=os.path.join(Train_Dir,subdir,filename)\n",
        "        allimgs.append(filepath)\n",
        "print(len(allimgs)) # Print the total number of images\n",
        "\n",
        "#split a test set from the dataset, train/test size = 80%/20%\n",
        "Numbers=len(allimgs)//5 \t#size of test set (20%)\n",
        "\n",
        "def mymovefile(srcfile,dstfile):\n",
        "    if not os.path.isfile(srcfile):\n",
        "        print (\"%s not exist!\"%(srcfile))\n",
        "    else:\n",
        "        fpath,fname=os.path.split(dstfile)    \n",
        "        if not os.path.exists(fpath):\n",
        "            os.makedirs(fpath)               \n",
        "        shutil.move(srcfile,dstfile)          \n",
        "\n",
        "Numbers\n",
        "\n",
        "val_imgs=random.sample(allimgs,Numbers)\n",
        "for img in val_imgs:\n",
        "    dest_path=img.replace(Train_Dir,Val_Dir)\n",
        "    mymovefile(img,dest_path)\n",
        "print('Finish creating test set')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFuw1CZOPzmA"
      },
      "source": [
        "**Resize the images 224*224 for better CNN training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9fg35scP28G",
        "outputId": "130a1dd2-c13f-4609-b7bf-70c0401cd26d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Finish resizing\n",
            "Finish resizing\n"
          ]
        }
      ],
      "source": [
        "def get_224(folder,dstdir):\n",
        "    imgfilepaths=[]\n",
        "    for root,dirs,imgs in os.walk(folder):\n",
        "        for this_img in imgs:\n",
        "            this_img_path=os.path.join(root,this_img)\n",
        "            imgfilepaths.append(this_img_path)\n",
        "    for this_img_path in imgfilepaths:\n",
        "        dir_name,filename=os.path.split(this_img_path)\n",
        "        dir_name=dir_name.replace(folder,dstdir)\n",
        "        new_file_path=os.path.join(dir_name,filename)\n",
        "        if not os.path.exists(dir_name):\n",
        "            os.makedirs(dir_name)\n",
        "        img=cv2.imread(this_img_path)\n",
        "        img=cv2.resize(img,(224,224))\n",
        "        cv2.imwrite(new_file_path,img)\n",
        "    print('Finish resizing'.format(folder=folder))\n",
        "\n",
        "DATA_DIR_224='/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train_A/'\n",
        "get_224(folder='/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/train/',dstdir=DATA_DIR_224)\n",
        "\n",
        "DATA_DIR2_224='/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/test_A/'\n",
        "get_224(folder='/content/drive/MyDrive/Transfer_IDS_IIoT/Datasets/test/',dstdir=DATA_DIR2_224)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}